Development of a Dimensionless Model for Pesticide Spraying in Agricultural Robotics

Dimensionless Model for Pesticide Spraying in Agricultural Robotics

Abstract

Pesticide application plays a vital role in modern agriculture by protecting crops from pests and diseases, thereby improving yield and food security. However, manual spraying methods continue to expose farmers and agricultural workers to hazardous chemicals, often leading to severe health complications such as respiratory disorders, skin irritation, and, in extreme cases, fatal poisoning. To address these challenges, this paper presents a systematic approach using the Buckingham Pi theorem to develop a dimensionless model that captures the essential relationships governing pesticide spraying processes. The study focuses on key variables such as pesticide volume and plant scan time—two critical factors that influence the efficiency and effectiveness of spraying operations. Through dimensional analysis, a generalized predictive equation is derived, enabling the characterization and optimization of spraying performance under varying operational conditions. The resulting model serves as a foundational tool for enhancing the control logic of robotic spraying systems, ensuring more uniform and targeted pesticide application. This approach minimizes wastage, reduces environmental contamination, and significantly improves operator safety by limiting human exposure. Furthermore, the integration of robotics with mathematical modeling underscores the potential for innovative, data-driven solutions in precision agriculture. The research contributes to the development of intelligent spraying systems that are both environmentally sustainable and economically viable, marking a significant advancement in agricultural automation and safety.